From Homo economicus, we derive many of the “laws” of economics. Aspects like the Law of Demand and Supply, marginal costs, marginal benefits, specialization, and exchange all arise from the study of Homo economicus. From these lessons, we can derive the utilization-maximizing function (marginal revenue = marginal cost), equilibrium results (quantity demanded = quantity supplied), indifference curves, and the like. The “Economics as a Science” side of economics.

Consider the following: You are playing catch with a friend in the park. Just a friendly game, tossing a baseball back and forth. Your friend tosses the baseball to you. Your mind and body, in fractions of seconds, calculates the speed of the ball, the angle of the throw, takes into account various external factors (such as wind) and estimates where the ball will be after T time passes. The end result is you raising your glove to catch the ball. In that short time, you become a physics machine, doing complex equations without even realizing it. Heck, you don’t even need to know the math you’ve done to do it! Physics is the science of trying to explain the actions taken during this game of catch. Physicists derive formulas to explain the various motions of you and your friend.

The situation is the same in economics. You go into the grocery store and see various items and various prices. Why do you choose to buy some items and not others? Preference, price, availability of various substitutes, etc all play into your thinking at an unconscious (and sometimes conscious) level. The indifference curves and Supply & Demand charts economists draw are a means of exploring and explaining these often sub-conscious items.

The idea of Homo economicus (and, perhaps its physics counterpart Homo physicus) is a simplification. It is not perfect. People will deviate from their prescribed outcomes. Clumsy me will miss the ball thrown at me. A person will make an impulse buy and regret it later. Deviations will occur. But the lessons we learn from Homo economicus are valuable in expanding human knowledge into the study of allocation of scarce resources. Great care must be taken both not to put too much emphasis on Homo economicus outcomes, but also not dismiss its usefulness.

The economist may, within limits, discuss this “choice,” provided he remains within what we have called “the logic of choice” [the individual decision-maker will select that option which stands highest in his ordering preference]. He cannot, however, plug in the Homo economicus introduced in his abstract models of economic behavior and then use this as the basis for constructing specific choice-influencing constraints aimed at welfare improvement. Individuals chose on the basis of their own preference orderings; they may, within limits, behave as the abstract theory of economics postulates. But rarely do they behave strictly as the automations of the analytical models. Yet this is precisely the unrecognized assumption that is implicit in most modern policy discussion.

As Buchanan says, this assumption that people behave as the perfect Homo economicus in the model is implicit, not explicit, in most modern policy discussions. It is from the “scientism” nature of much of modern economics that makes this assumption implicit. The good economist recognizes this assumption is a tool for simplifying a complex world. This assumption, and the models that come from it, give us valuable insights into how the world works. But it is not the be-all and end-all. Using models, and only models, for policy planning, no matter how much mathematics they have in them, will ultimately lead to disappointment.

The Concord Monitor reports on your letter to the FDA urging the agency to speed review and approval of generic EpiPen substitutes. I can only hope the FDA takes your advice. As any economist will tell you, competition among producers drive down the costs of goods. When competition is restricted, in this case by the FDA’s approval process, it allows a firm to earn “extra-normal” profits, which drives up the monetary costs for all of us. I also hope you do not stop at just EpiPens but urge the FDA to speed up (or eliminate) their approval of all pending drugs. The more drugs that hit the market, the more competition there will be, and the sooner we can address rising medical care costs here in the United States.

One of the best teachers I ever had was Mr. Garabedian. He was my pre-calculus professor in undergrad. Mr. Garabedian had a strict “no calculators” rule in his class. Not because he was some old hard-ass who was afraid of technology, but just the opposite: he was well aware of how powerful technology was and how easily a relatively cheap calculator could produce the answers to mathematical problems. In his career, he noted that students could become very competent at using calculators (and later Google) to solve math problems. Memorizing formulas was no longer necessary. But this, in turn, lead to students not understanding the reasoning behind the mathematics. A student could determine 4 * 4 = 16, but not explain why. Conversely, students would get a wrong answer supplied by the calculator but not have the ability to know/detect the answer was wrong, or where the error might be. So, his class focused primarily on the intuition aspect of mathematics; the logic behind the framework. I am not a great mathematician; I never have been and never will be. But this method of teaching broke through to me and things that were jumbled messes of numbers and letters suddenly became clear. Knowing the intuition, I’d be able to work through mathematical problems without knowing formulas, and be able to tell when an answer didn’t make sense. Furthermore, this helped me become a better student by knowing when to seek help.

In short, Mr. Garabedian taught me not just math, but to ask the question “does this outcome make sense?”

Asking that question is what separates the thinker from merely the purveyor of science. All too often, I come across someone who is very smart point to some mathematical model or some chaotic theory and claim, with a perfectly straight face, that their model/theory (simply by the virtue of being a model coupled with mathematics) provides an outcome contrary to what might seem logical. This is true not only of economics (which does have its fair share of “sciencism”), but of politics, and business, and sociology, psychology, biology, chemistry, etc. In short, they fail to ask the natural follow up question: does this outcome make sense? Does it make sense than a minimum wage hike of 107% would have no impact on employment margins (regardless of what the model says)? Does it make sense that the world is so chaotic that there are no such things as trade-offs (regardless of what the theory says)? Does it make sense to alienate an entire demographic of voters (regardless what the voting models say)? The list goes on.

To be clear, none of this is to say that mathematics and models aren’t important. They are. But they are just one tool in our toolbox and one must remember that, ultimately, data never speak for themselves and mathematics is, ultimately, a logical field. When confronted with data, no matter how rigorous or precise your model might be, be sure to ask the question “does this make sense?”

The core economic question is how decisions are made about how scarce resources will be allocated among competing uses. Scarcity necessitates choice, which in turn implies trade-offs since one use of scarce resources precludes another.

The fact resources are scarce is simply due to the face we do not live in Eden.

One of my favorite shows on TV isRick and Morty. The show’s titular characters are Rick, an alcoholic, cynical, mad-scientist type and his grandson, Morty. The show has many great moments, but one of my favorites is after Rick makes a particularly nasty mistake. His reaction is pretty stoic:

“Ok…Well, sometimes science is more art than science, Morty. A lot of people don’t get that.”

What I like about this quote, particularly in the context it is given, is the reminder to all scientists that we are, ultimately, working off of guess work, theory, and observations. It’s not as precise as we’d like. What’s more, his throw-away line at the end (“A lot of people don’t get that,”) is a jab at laymen (and even some scientists) who look towards the sciences to provide clear-cut answers and policies. But the reality is there is still so much uncertainty, so much more to know, and the world is not often precise.

Rick’s attitude regarding the hard sciences can easily be applied (perhaps to even a greater degree) to social sciences like economics. In fact, his attitude is identical to that of F.A. Hayek in describing what he called “the pretense of knowledge,” the idea that things can be directed by someone(s) with appropriate amounts of knowledge. Furthermore, Rick’s chide that “a lot of people don’t get that,” could have been spoken by Hayek himself, especially to those who believe “precise mathematical models” are necessary and sufficient.

Economics is very much more art than science sometimes. Those who look to us economists to prove clean-cut solutions to the world will be sorely disappointed, and those economists to believe they can direct the world accurately are not much more than snake-oil salesmen. Economics can offer guidance, but to expect more is to open one’s self up to disappointment.

As an aside, this is one of the few moments in the show where Rick admits he’s wrong. Rick considers himself a genius, often above reproach. He is very much mathematically-driven (at one point, telling his grandkids “You’re both pieces of shit, and I can prove it mathematically,”) but even he admits there is limit to what math can tell us.

Writing at libertarianism.org, Jason Kuznicki of the Cato Institute writes about American poor. The whole article is interesting, but I want to call attention to one particular quote:

The overwhelming majority of the poor in the United States enjoy technological wonders that didn’t even exist a few decades ago. Outside the free market/liberal democratic synthesis, essentially no other social system has ever delivered as much — because almost none of them can produce a steady stream of new technological innovations in the first place, let alone distribute them to the poor.

I suspect many will discount this quote by simply saying “sure we can buy more iPads, but not important stuff!” but I think it bares remembering that some of the technology referenced includes many household items, like dishwashers, washing machines, refrigerators, cars, clothing, food, education, and so on. “Technology” does not mean just gadgets.

The American poor are not poor by global standards (or even some other 1st World standards). They’ve gotten to this level of “poverty” through the technological achievements Kuznicki discusses.